CMU ML lunch: Topics in Spam Filtering

Data: 9/24
Title: Topics in Spam Filtering
Speaker: D. Sculley, Tufts University

Abstract:
This talk will examine three recent inquiries in machine-learning
based filtering of spam emails. First, we will examine a long
standing debate in the spam filtering community, and show that
online support vector machines (SVMs) do, indeed, give state of
the art performance on spam filtering tasks. Second, we show how
to reduce the cost of online SVMs with several relaxations, which
yield nearly equivalent results at greatly reduced computational
cost. Third, we investigate the use of online active learning
methods for spam filtering, which both reduce the number of labels
needed for strong filtering performance and enable a variety of
useful user-interface options. Finally, we investigate the problem
of one-sided feedback, caused when a potentially lazy user only
labels messages that appear in the inbox, and never gives feedback
on messages that are predicted to be spam.

Related papers:
online active learning for spam filtering
Relaxed Online SVMs for Spam Filtering